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Artificial intelligence and causal inference /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial intelligence and causal inference /Momiao Xiong.
作者:	Xiong, Momiao,
面頁冊數:	1 online resource(xxv, 368pages) :illustrations.
標題:	Artificial intelligence.
電子資源:	https://www.taylorfrancis.com/books/9781003028543
電子資源:	https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781000531732
電子資源:	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3002156
ISBN:	9781003028543$qelectronic book

Artificial intelligence and causal inference /
Xiong, Momiao,

Artificial intelligence and causal inference /Momiao Xiong. - First edition. - 1 online resource(xxv, 368pages) :illustrations. - Chapman & Hall/CRC machine learning & pattern recognition. - Chapman & Hall/CRC machine learning & pattern recognition series..

Includes bibliographical references and index.

Deep neural networks -- Gaussian processes and learning dynamic for wide neural networks -- Deep generative models -- Generative adversarial networks -- Deep learning for causal inference -- Causal inference in time series -- Deep learning for counterfactual inference and treatment effect estimation -- Reinforcement learning and causal inference.

"Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine"--

ISBN: 9781003028543$qelectronic book

Source: 9781003028543Taylor & Francis

LCCN: 2021040989Subjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .X56 2022

Dewey Class. No.: 006.3/1

Artificial intelligence and causal inference /
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245 1 0 $a Artificial intelligence and causal inference / $c Momiao Xiong.
250 $a First edition.
264 1 $a Boca Raton, FL : $b A Chapman & Hall Book, CRC Press, $c 2022.
264 4 $c ©2022
300 $a 1 online resource(xxv, 368pages) : $b illustrations.
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490 1 $a Chapman & Hall/CRC machine learning & pattern recognition
504 $a Includes bibliographical references and index.
505 0 $a Deep neural networks -- Gaussian processes and learning dynamic for wide neural networks -- Deep generative models -- Generative adversarial networks -- Deep learning for causal inference -- Causal inference in time series -- Deep learning for counterfactual inference and treatment effect estimation -- Reinforcement learning and causal inference.
520 $a "Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine"-- $c Provided by publisher.
588 $a Description based on online resource; title from digital title page (viewed on March 03, 2022).
650 0 $a Artificial intelligence. $3 194058
650 0 $a Causation. $3 178851
650 0 $a Inference. $3 187753
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650 6 $a Intelligence artificielle. $3 299112
650 6 $a Inférence (Logique) $3 966399
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650 7 $a COMPUTERS / Machine Theory $2 bisacsh $3 564229
650 7 $a MATHEMATICS / Probability & Statistics / General $2 bisacsh $3 966309
650 7 $a Artificial intelligence $3 252958
650 7 $a Causation $3 786625
650 7 $a Inference $v Congresses. $3 949845
776 0 8 $i Print version: $a Xiong, Momiao. $t Artificial intelligence and causal inference $b First edition. $d Boca Raton : CRC Press, 2022 $z 9780367859404 $w (DLC) 2021040988
830 0 $a Chapman & Hall/CRC machine learning & pattern recognition series. $3 380884
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856 4 0 $3 EBSCOhost $u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3002156
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https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781000531732
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